Inverter System: A Solution to Improve the Efficiency of New Energy Generation in Factories

The report mainly analyzes whether the inverter system could improve the efficiency of converting new energy into factory electricity based on McLuhan's laws of media theory. Firstly, the report asserts the significance of using new energy and the importance of utilizing the inverter system to improve the power conversion of new energy in factories. Secondly, it mainly describes McLuhan's theory from four different aspects. In addition, according to the four aspects of McLuhan's theory, the rationality and feasibility of the inverter system solution are analyzed. Then, it is concluded that the inverter system can well improve the conversion efficiency of new energy generation in factories. Finally, this paper claims suggestions from two different perspectives to promote the development of the inverter system

Flavin-N5OOH: A most powerful nucleophile and base in nature

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Application of LSTM and CONV1D LSTM Network in Stock Forecasting Model

Predicting the direction of the stock market has always been a huge challenge. Also, the way of forecasting the stock market reduces the risk in the financial market, thus ensuring that brokers can make normal returns. Despite the complexities of the stock market, the challenge has been increasingly addressed by experts in a variety of disciplines, including economics, statistics, and computer science. The introduction of machine learning, in-depth understanding of the prospects of the financial market, thus doing many experiments to predict the future so that the stock price trend has different degrees of success. In this paper, we propose a method to predict stocks from different industries and markets, as well as trend prediction using traditional machine learning algorithms such as linear regression, polynomial regression and learning techniques in time series prediction using two forms of special types of recursive neural networks: long and short time memory (LSTM) and spoken short-term memory

Collaborative Graph Neural Networks for Attributed Network Embedding

Graph neural networks (GNNs) have shown prominent performance on attributed network embedding. However, existing efforts mainly focus on exploiting network structures, while the exploitation of node attributes is rather limited as they only serve as node features at the initial layer. This simple strategy impedes the potential of node attributes in augmenting node connections, leading to limited receptive field for inactive nodes with few or even no neighbors. Furthermore, the training objectives (i.e., reconstructing network structures) of most GNNs also do not include node attributes, although studies have shown that reconstructing node attributes is beneficial. Thus, it is encouraging to deeply involve node attributes in the key components of GNNs, including graph convolution operations and training objectives. However, this is a nontrivial task since an appropriate way of integration is required to maintain the merits of GNNs. To bridge the gap, in this paper, we propose COllaborative graph Neural Networks--CONN, a tailored GNN architecture for attribute network embedding. It improves model capacity by 1) selectively diffusing messages from neighboring nodes and involved attribute categories, and 2) jointly reconstructing node-to-node and node-to-attribute-category interactions via cross-correlation. Experiments on real-world networks demonstrate that CONN excels state-of-the-art embedding algorithms with a great margin

Local Integral Estimates for Quasilinear Equations with Measure Data

Local integral estimates as well as local nonexistence results for a class of quasilinear equations -Δpu=σP(u)+ω for p>1 and Hessian equations Fk-u=σP(u)+ω were established, where σ is a nonnegative locally integrable function or, more generally, a locally finite measure, ω is a positive Radon measure, and P(u)~exp⁡αuβ with α>0 and β≥1 or P(u)=up-1

Multi-Task Learning for Post-transplant Cause of Death Analysis: A Case Study on Liver Transplant

Organ transplant is the essential treatment method for some end-stage diseases, such as liver failure. Analyzing the post-transplant cause of death (CoD) after organ transplant provides a powerful tool for clinical decision making, including personalized treatment and organ allocation. However, traditional methods like Model for End-stage Liver Disease (MELD) score and conventional machine learning (ML) methods are limited in CoD analysis due to two major data and model-related challenges. To address this, we propose a novel framework called CoD-MTL leveraging multi-task learning to model the semantic relationships between various CoD prediction tasks jointly. Specifically, we develop a novel tree distillation strategy for multi-task learning, which combines the strength of both the tree model and multi-task learning. Experimental results are presented to show the precise and reliable CoD predictions of our framework. A case study is conducted to demonstrate the clinical importance of our method in the liver transplant

Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint

Clinical trials are indispensable in developing new treatments, but they face obstacles in patient recruitment and retention, hindering the enrollment of necessary participants. To tackle these challenges, deep learning frameworks have been created to match patients to trials. These frameworks calculate the similarity between patients and clinical trial eligibility criteria, considering the discrepancy between inclusion and exclusion criteria. Recent studies have shown that these frameworks outperform earlier approaches. However, deep learning models may raise fairness issues in patient-trial matching when certain sensitive groups of individuals are underrepresented in clinical trials, leading to incomplete or inaccurate data and potential harm. To tackle the issue of fairness, this work proposes a fair patient-trial matching framework by generating a patient-criterion level fairness constraint. The proposed framework considers the inconsistency between the embedding of inclusion and exclusion criteria among patients of different sensitive groups. The experimental results on real-world patient-trial and patient-criterion matching tasks demonstrate that the proposed framework can successfully alleviate the predictions that tend to be biased

Exosomes in pathogenesis, diagnosis, and therapy of ischemic stroke

Ischemic stroke is one of the major contributors to death and disability worldwide. Thus, there is an urgent need to develop early brain tissue perfusion therapies following acute stroke and to enhance functional recovery in stroke survivors. The morbidity, therapy, and recovery processes are highly orchestrated interactions involving the brain with other tissues. Exosomes are natural and ideal mediators of intercellular information transfer and recognized as biomarkers for disease diagnosis and prognosis. Changes in exosome contents express throughout the physiological process. Accumulating evidence demonstrates the use of exosomes in exploring unknown cellular and molecular mechanisms of intercellular communication and organ homeostasis and indicates their potential role in ischemic stroke. Inspired by the unique properties of exosomes, this review focuses on the communication, diagnosis, and therapeutic role of various derived exosomes, and their development and challenges for the treatment of cerebral ischemic stroke

Open Design and 3D Printing of Face Shields: The Case Study of a UK-China Initiative

At the start of the COVID-19 outbreak, many countries lacked personal protective equipment (PPE) to protect healthcare workers. To address this problem, open design and 3D printing technologies were adopted to provide much-in-need PPEs for key workers. This paper reports an initiative by designers and engineers in the UK and China. The case study approach and content analysis method were used to study the stakeholders, the design process, and other relevant issues such as regulation. Good practice and lessons were summarised, and suggestions for using distributed 3D printing to supply PPEs were made. It concludes that 3D printing has played an important role in producing PPEs when there was a shortage of supply, and distributed manufacturing has the potential to quickly respond to local small-bench production needs. In the future, clearer specification, better match of demands and supply, and quicker evaluation against relevant regulations will provide efficiency and quality assurance for 3D printed PPE supplies